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3 edition of Object-oriented control system design using on-line training of artificial neural networks found in the catalog.

Object-oriented control system design using on-line training of artificial neural networks

Object-oriented control system design using on-line training of artificial neural networks

final report, grant no. NAG3-1661, December 01, 1996 - April 30, 1997 ... Howard University/NASA Lewis cooperative research studies

by

  • 254 Want to read
  • 1 Currently reading

Published by Howard University, College of Engineering, Architecture and Computer Sciences, Electrical Engineering Dept., [National Aeronautics and Space Administration, National Technical Information Service, distributor in Washington, DC, Springfield, Va .
Written in English

    Subjects:
  • Aerospace engineering.,
  • Control systems design.,
  • Controllers.,
  • Neural nets.,
  • Artificial intelligence.,
  • On-line systems.,
  • Real time operation.,
  • Object-oriented programming.

  • Edition Notes

    Other titlesObject oriented control system design using online training of artificial neural networks
    StatementAhmed Rubaai, principal investigator.
    Series[NASA contractor report] -- 206526, NASA contractor report -- NASA CR-206526.
    ContributionsUnited States. National Aeronautics and Space Administration.
    The Physical Object
    FormatMicroform
    Pagination1 v.
    ID Numbers
    Open LibraryOL17127137M

    This tutorial text provides the reader with an understanding of artificial neural networks (ANNs), and their application, beginning with the biological systems which inspired them, through the learning methods that have been developed, and the data collection processes, to the many ways ANNs are being used today. The material is presented with a minimum of math (although the mathematical 3/5(2).   All the examples generated in the book are provided in the form of illustrative source code, which merges object-oriented programming (OOP) concepts and neural network features to enhance your learning experience. Features: Learn to build amazing projects using neural networks including forecasting the weather and pattern recognition. Neural networks are based on real numbers, with the value of the core and of the axon typically being a representation between and 1. Perceptrons A Perceptron is an algorithm for supervised learning of binary classifiers which are functions that can decide whether or not input, represented by a vector of numbers, belongs to some specific class.


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Object-oriented control system design using on-line training of artificial neural networks Download PDF EPUB FB2

The object-oriented motor drive control system design tool, as shown in figure 1, is comprised mainly of four parts: motor modelling, dynamic learning, neural control strategy and controller optimization.

The design is based on motor types and knowledge base including object models, a control library and a database. Each motor or. Get this from a library. Object-oriented control system design using on-line training of artificial neural networks: final report, grant no. NAG, Decem - Ap Howard University/NASA Lewis cooperative research studies.

[Ahmed Rubaai; United States. National Aeronautics and Space Administration.]. Abstract. The aim of this report is to present an object-oriented approach to the design of a neural network simulation system. Although there are several object-oriented neural network systems available, only a few presents their design by using a consistent and uniform object-oriented methodology.

The field of neural networks is being investigated by many researchers in order to provide solutions to difficult problems in the area of manufacturing systems. Computer simulation of neural networks is an important part of this investigation.

This paper applies concepts from an important trend in software engineering research, namely object-oriented programming, to model neural by: 1. On-line Training of Neural Networks: A Sliding Window Approach for the Levenberg-Marquardt Algorithm. Neural network based control system design toolkit for use with matlab, versiontechnical Antunes A., Vieira J., Mota A.M.

() On-line Training of Neural Networks: A Sliding Window Approach for the Levenberg-Marquardt Algorithm Cited by: 8. Taguchi Methods (and other orthogonal arrays) may be used to train small Artificial Neural Networks very quickly in a variety of tasks. These include, importantly, Control Systems.

Training artificial neural networks to learn a nondeterministic game Thomas E. Portegys DigiPen Institute of Technology Willows Rd. NE, Redmond, WA, USA [email protected] Abstract.

It is well known that artificial neural networks (ANNs) can learn deterministic automata. Learning nondeterministic automata is another matter. This is. used as function approximator, neural networks have been found to be particularly useful for controlling highly uncertain, nonlinear and complex systems.

Neural control strategies can be broadly classified into off-line and on-line schemes based on how the parameters of the network are tuned. When the neural controller operates in an on-line mode, it has no a priori knowledge of the system to be. control. These controllers demonstrate the variety of ways in which multilayer perceptron neural networks can be used as basic building blocks.

We demonstrate the practical implementation of these controllers on three applications: a continuous stirred tank reactor, a robot arm, and a magnetic levitation system. INTRODUCTION In this tutorial. Neural Networks in Control Systems Tehv ee r-increasinteg c hnologicda el- mands of our modem society require inno- vative approaches to highly demanding con- trol problems.

Artificial neural networks with theirm assivep arallelisma ndl earningc a- pabilities offer thep romise of betters olu. Artificial Neural Networks and Deep Learning Christian Borgelt Training • General Neural Networks Structure, Operation, Training • Multi-layer Perceptrons Definition, Function Approximation, Gradient Descent, Backpropagation, Variants, Sensitivity Analysis • Deep Learning • Symbol-based expert systems tend to get slower with.

This paper examines various design issues and decisions that are involved in the process of developing Artificial Neural Networks (ANN) applications for use outside the laboratory environment, i.e.

in the “real world”. To assist in this process a new design methodology is proposed for developing both ANN Systems and applications. 15 Supervised vs. Unsupervised Learning. Supervised Learning " Estimate an unknown mapping from known input- output pairs " Learn fw from training set D={(x,y)} s.t.

" Classification: y is discrete " Regression: Object-oriented control system design using on-line training of artificial neural networks book is continuous " Example: Hand-written numeral recognition " x: a scanned numeral (vector of gray-scale values) " y: class of the numeral (0, 1,or 9).

communities, etc. Which other systems could you see as a network. Why. Artificial neural networks One typ e of network see s the nodes a s ‘a rtificia l neuro ns’. These are called ar tificial neural networks (ANNs). An artificial neuron is a computational model inspired in the na tur al ne ur by: Neural Networks and Its Application in Engineering Oludele Awodele and Olawale Jegede Dept.

of Computer Science and Mathematics, Babcock University, Nigeria [email protected] ; [email protected] Abstract Neural Network (NN) has emerged over the years and has made remarkable contribution to the advancement of various fields of by: The author has employed object-oriented design and object-oriented programming concepts to develop a set of foundation neural network classes, and shows how these classes can be used to implement a variety of neural network architectures with a great deal of ease and flexibility/5(17).

Fierro and Lewis () developed a neural network based model by combining the backstepping tracking technique and a torque controller, using a multi-layer feedforward neural network, where the neural network can learn the dynamics of the mobile robot by its on-line learning.

But the control algorithm and the neural network learning algorithm Cited by: 7. Control problems offer an industrially important application and a guide to understanding control systems for those working in Neural Networks. Neural Systems for Control represents the most up-to-date developments in the rapidly growing aplication area of neural networks and focuses on research in natural and artifical neural systems directly applicable to control or making use of modern.

An Object-Oriented Framework for the Simulation of Neural Nets Design Highlights User Interface The user interface is text oriented and may be used interactively as well as script driven. This implies that any command that the user may choose interactively can also be used in a command file that is called non-interactively.

Object-oriented software prediction using neural networks Article in Information and Software Technology 49(5) May with Reads How we measure 'reads'. The human nervous system consists of billions of neurons of various types and lengths relevant to their location in the body (Schalkoff, ).Fig.

1a shows a schematic of an oversimplified biological neuron with three major functional units — dendrites, cell body, and axon. The cell body has a nucleus that contains information about heredity traits, and a plasma that holds the molecular Cited by: The author has employed object-oriented design and object-oriented programming concepts to develop a set of foundation neural network classes, and shows how these classes can be used to implement a variety of neural network architectures with a great deal of ease and by: Asil Alkaya, Miraç Bayhan, The classification of a simulation data of a servo system via evolutionary artificial neural networks, Proceedings of the Intelligent computing 5th international conference on Emerging intelligent computing technology and applications, September, Ulsan, South KoreaCited by: Artificial Neural Network ANN is an efficient computing system whose central theme is borrowed from the analogy of biological neural networks.

ANNs are also named as “artificial neural systems,” or “parallel distributed processing systems,” or “connectionist systems.” ANN acquires a large collection of units that are interconnected.

Free download A Guide to Developing Client/Server SQL Applications (Morgan Kaufmann Series in Data Management Systems) Free download A Work in Progress: A Memoir Free download Access 97 Programming for Windows For Dummies.

I am learning about Neural Networks and back-propagation. I think I understand how the network works, in terms of input, output, hidden layers, weights, bias etc However, I still don't fully understand how to design a network to fit a problem.

APPLICATION OF ARTIFICIAL NEURAL NETWORKS FOR ASSESSING THE. TESTABILITY OF OBJECT ORIENTED SOFTWARE. Yogesh Singh,Anju Saha. Abstract — In this paper, we present the application of neural networks for predicting the software testability using the object oriented design metrics.

The testability is generally measured in terms of the effort. This report deals with the object-oriented model development of a neuro-controller design for permanent magnet (PM) dc motor drives. The system under study is. The main objective is to develop a system to perform various computational tasks faster than the traditional systems.

This tutorial covers the basic concept and terminologies involved in Artificial Neural Network. Sections of this tutorial also explain the architecture as well as the training algorithm of various networks used in ANN.

Audience. 3. INTRODUCTION• “Neural“ is an adjective for neuron, and “network” denotes a graph like structure.• Artificial Neural Networks are also referred to as “neural nets”, “artificial neural systems”, “parallel distributed processing systems”, “connectionist systems”.• For a computing systems to be called by these.

Artificial neural networks (ANN) or connectionist systems are computing systems vaguely inspired by the biological neural networks that constitute animal brains. Such systems "learn" to perform tasks by considering examples, generally without being programmed with task-specific rules.

of multi-layer feed-forward neural networks are discussed. Improvements of the standard back-propagation algorithm are re- viewed. Example of the use of multi-layer feed-forward neural networks for prediction of carbon NMR chemical shifts of alkanes is Size: 1MB.

This makes it a versatile programming language for neural networks. This book gives you a complete walkthrough of the process of developing basic to advanced practical examples based on neural networks with Java. You will first learn the basics of neural networks and their process of learning.

We then focus on what Perceptrons are and their. Neural networks in medicine • Artificial Neural Networks (ANN) are currently a 'hot' research area in medicine and it is believed that they will receive extensive application to biomedical systems in the next few years.

At the moment, the research is mostly on modelling parts of File Size: 2MB. The Multiple Principal Component (MPC) Fuzzy Neural Network for tool condition monitoring in machining under varying cutting conditions is proposed.

This approach is based on three major components of “soft computation,” namely fuzzy logic, neural network, and probability by:   Classification of four different types of real-life images using an Artificial Neural Network with Back-Propagation learning.

While the larger chapters should provide profound insight into a paradigm of neural networks (e.g. the classic neural network structure: the perceptron and its learning simulation design goal.

Those of you who are up for learning by doing and/or have to use a fast and stable neural networks implementation for some reasons, should. Neural Network Design (2nd Edition), by the authors of the Neural Network Toolbox for MATLAB, provides a clear and detailed coverage of fundamental neural network architectures and learning book gives an introduction to basic neural network architectures and learning rules.

Emphasis is placed on the mathematical analysis of these networks, on methods of training them and on their. 4.) Object Oriented Language. Using Micro Python fills in a number of gaps in the C programming language, including the ability to create classes and use object oriented programming techniques.

Embedded software developers using Micro Python can take advantage of modern day programming constructs such as inheritance and : Jacob Beningo. The introduction of artificial intelligence, neural networks, and fuzzy logic into industry has given a new perspective to manufacturing processes in the U.S.

and abroad. To help readers keep pace, - Selection from Design and Implementation of Intelligent Manufacturing Systems: From Expert Systems, Neural Networks, to Fuzzy Logic [Book].

Learning in Artificial Neural Networks One of the most impressive features of artificial neural networks is their ability to learn. You may recall from the previous tutorial that artificial neural networks are inspired by the biological nervous system, in particular, the human brain. One of the most interesting characteristics of the human.

Hand Written Character Recognition Using Neural Networks 1. Hand Written Character Recognition Using Neural Network Chapter 1 1 Introduction The purpose of this project is to take handwritten English characters as input, process the character, train the neural network algorithm, to recognize the pattern and modify the character to a beautified version of the input.Dynamic thresholds and attractor neural networks.- Use of genetic algorithms in neural networks definition.- Simulated evolution of modular networks.- Computational experiments with Boltzmann Machines.- An adaptive resonance theory architecture for the automatic recognition of on-line handwritten symbols of a mathematical editor